Hands-on Session: Reconstructing Past Social Networks from Inscriptions

Event: Epigraphy.info IX, 2-4 April 2025, Aarhus, Denmark. See https://epigraphy.info/workshop_9/ for details.

This script demonstrates how to convert a real Latin inscription into a network using the famous Rabirii inscription from Via Appia (EDR103440: http://www.edr-edr.it/edr_programmi/res_complex_comune.php?do=book&id_nr=EDR103440).

1 Initial setup

# Install required packages if not already installed
if (!requireNamespace("igraph", quietly = TRUE)) install.packages("igraph")
if (!requireNamespace("dplyr", quietly = TRUE)) install.packages("dplyr")
if (!requireNamespace("ggplot2", quietly = TRUE)) install.packages("ggplot2")
if (!requireNamespace("ggraph", quietly = TRUE)) install.packages("ggraph")
if (!requireNamespace("networkD3", quietly = TRUE)) install.packages("networkD3")

# Load required libraries
library(igraph)      # For network creation and analysis
library(dplyr)       # For data manipulation
library(ggplot2)     # For visualization
library(ggraph)      # For advanced network visualization
library(networkD3)   # For interactive visualization

2 The inscription and its context

Information source: EDR103440: http://www.edr-edr.it/edr_programmi/res_complex_comune.php?do=book&id_nr=EDR103440

CIL number: CIL 6.2246.

Description: Funerary monument, with portrait relief, for Gaius Rabirius Hermodorus, Rabiria Demaris, and Usia Prima, a priestess of Isis. Dated between 50 BC / 40 BC (archaeological context and palaeography, EDR).

Images:

Latin text of the inscription: (as recorded in EDR, including the Leiden markup)

〈:columna I〉 C(aius) Rabirius Post(umi) l(ibertus) Hermodorus.

〈:columna II〉 Rabiria Demaris.

〈:columna III〉 《Usia Prima, sac(erdos)》 《Isidis》.

# Latin text of the inscription (actual text from EDR103440):
latin_text <- "〈:columna I〉
C(aius) Rabirius Post(umi) l(ibertus)
Hermodorus.
〈:columna II〉
Rabiria
Demaris.
〈:columna III〉
《Usia Prima, sac(erdos)》
《Isidis》."

english_translation <- "Gaius Rabirius Hermodorus, freedman of Postumus, Rabiria Demaris, Usia Prima, priestess of Isis"

3 Identify people and relationships

3.1 Step 1: Identify individuals mentioned in the inscription

# Let's identify each person and assign them an ID

people <- data.frame(
  id = 1:4,
  name = c("Gaius Rabirius Hermodorus", 
           "Postumus Rabirius", 
           "Rabiria Demaris", 
           "Usia Prima"),
  gender = c("male", "male", "female", "female"),
  role = c("freedman", "former master", "wife", "priestess"),
  status = c("freedman", "freeborn", "unknown", "unknown"),
  additional_info = c("", "", "", "priestess of Isis"),
  stringsAsFactors = FALSE
)

# Print the people we've identified
print(people)

##   id                      name gender          role   status   additional_info
## 1  1 Gaius Rabirius Hermodorus   male      freedman freedman                  
## 2  2         Postumus Rabirius   male former master freeborn                  
## 3  3           Rabiria Demaris female          wife  unknown                  
## 4  4                Usia Prima female     priestess  unknown priestess of Isis

3.2 Step 2: Identify relationships between these individuals

relationships <- data.frame(
  from = c(1, 2, 1, 4),
  to = c(2, 1, 3, NA),
  relationship = c("freedman_of", "patron_of", "partner_of", "priestess_of_deity"),
  certainty = c("certain", "certain", "uncertain", "certain"),
  additional_info = c("", "", "possibly family members", "priestess of Isis"),
  stringsAsFactors = FALSE
)

# Print the relationships we've identified
print(relationships)

##   from to       relationship certainty         additional_info
## 1    1  2        freedman_of   certain                        
## 2    2  1          patron_of   certain                        
## 3    1  3         partner_of uncertain possibly family members
## 4    4 NA priestess_of_deity   certain       priestess of Isis

3.3 Step 3: Create the network

# Remove NA relationships for network creation
relationships_clean <- relationships %>% 
  filter(!is.na(to))

# Create the graph/network object
inscription_network <- graph_from_data_frame(relationships_clean, directed = TRUE, vertices = people)

# Print basic network information
print(inscription_network)

## IGRAPH 8791e9e DN-- 4 3 -- 
## + attr: name (v/c), gender (v/c), role (v/c), status (v/c),
## | additional_info (v/c), relationship (e/c), certainty (e/c),
## | additional_info (e/c)
## + edges from 8791e9e (vertex names):
## [1] Gaius Rabirius Hermodorus->Postumus Rabirius        
## [2] Postumus Rabirius        ->Gaius Rabirius Hermodorus
## [3] Gaius Rabirius Hermodorus->Rabiria Demaris

3.4 Step 4: Basic visualization

cat("\nStep 4: Visualizing the network\n")

## 
## Step 4: Visualizing the network

# Set plot parameters
par(mar = c(0, 0, 2, 0))  # Adjust margins for better visualization

# Plot the network
plot(inscription_network, 
     vertex.label = V(inscription_network)$name,
     vertex.color = ifelse(V(inscription_network)$gender == "male", "blue", "red"),
     vertex.size = 20,
     vertex.label.cex = 0.9,
     edge.arrow.size = 0.5,
     edge.label = E(inscription_network)$relationship,
     edge.label.cex = 0.9,
     layout = layout_with_fr(inscription_network),
     main = "Network from EDR103440 Inscription")

3.5 Step 5: Advanced visualization with ggraph

# Create a more sophisticated visualization
inscription_plot <- ggraph(inscription_network, layout = 'fr') + 
  geom_edge_link(aes(label = relationship, 
                     color = certainty), 
                 arrow = arrow(length = unit(4, 'mm')), 
                 end_cap = circle(5, 'mm'),
                 angle_calc = 'along',
                 label_size = 3) + 
  geom_node_point(aes(color = gender, size = 5)) + 
  geom_node_text(aes(label = name), repel = TRUE, size = 3.5) +
  scale_edge_color_manual(values = c("certain" = "black", "uncertain" = "gray90")) +
  scale_color_manual(values = c("male" = "blue", "female" = "red")) +
  theme_graph() +
  ggtitle("Social Relations in EDR103440 Inscription") +
  theme(legend.position = "bottom")

# Print the plot
print(inscription_plot)

4 More complex data - family ties

Using AI (Claude.ai) generated dataset of 74 individuals and their family ties from a ficticious corpus called INS.

Dataset Structure:

• Basic Information: Names (praenomen, nomen, cognomen), family ID, birth/death years, gender
• Family Connections: Father, mother, spouse, and patron IDs
• Social Context: Status (patrician/plebeian)
• Archaeological Data: Inscription ID, location, and type

The Three Families:

• Valerii (family_id 1): A patrician family with branches in Rome, Ostia, Pompeii and Herculaneum
• Cornelii (family_id 2): A prominent patrician family centered in Rome and Capua
• Tullii (family_id 3): A family with both patrician and plebeian members, primarily in Rome

Interconnections:

• Marriage alliances between families (example: person 53, Valeria Messalina, connects the Valerii to the Cornelii)
• Multiple generations (spanning approximately 200 years)
• Various inscription types (funerary, honorary, votive)

4.1 Load the data

# Load necessary libraries
library(igraph)
library(dplyr)
library(readr)
library(ggplot2)

# Read the CSV data
  family <- read.csv("../data/latin-inscriptions-family-data.txt", stringsAsFactors = FALSE)

# display the data
family

4.2 Process the data

# Clean up the data - replace NA strings with actual NA values
family[family == "NA"] <- NA

# Create family relationships dataframe
relationships <- data.frame(
  from = character(),
  to = character(),
  relation = character(),
  stringsAsFactors = FALSE
)

# Add parent-child relationships
# Father-child
father_child <- family %>%
  filter(!is.na(father_id)) %>%
  select(father_id, person_id)
father_child$relation <- "parent"
names(father_child) <- c("from", "to", "relation")

# Mother-child
mother_child <- family %>%
  filter(!is.na(mother_id)) %>%
  select(mother_id, person_id)
mother_child$relation <- "parent"
names(mother_child) <- c("from", "to", "relation")

# Spouse relationships
spouse <- family %>%
  filter(!is.na(spouse_id) & person_id < spouse_id) %>%
  select(person_id, spouse_id)
spouse$relation <- "spouse"
names(spouse) <- c("from", "to", "relation")

# Combine all relationships
relationships <- rbind(relationships, father_child, mother_child, spouse)

# Create a lookup table for person names
person_lookup <- family %>%
  mutate(full_name = paste(praenomen, nomen, cognomen)) %>%
  select(person_id, full_name, family_id, gender, birth_year, death_year)

person_lookup

4.3 Visualise the data

Main Network Visualization shows all three families with their interconnections, using: - Different colors for each family (Valerii in blue, Cornelii in green, Tullii in salmon) - Different shapes for gender (squares for males, circles for females) - Different line styles for relationship types (gray for parent-child, red for spouse

The visualizations use the Fruchterman-Reingold layout algorithm, which works well for family networks as it tends to place connected nodes closer together.

# Create the network graph
g <- graph_from_data_frame(d = relationships, vertices = person_lookup, directed = TRUE)

# Set vertex attributes
V(g)$color <- ifelse(V(g)$family_id == 1, "skyblue", 
                    ifelse(V(g)$family_id == 2, "lightgreen", "salmon"))
V(g)$shape <- ifelse(V(g)$gender == "M", "square", "circle")
V(g)$size <- 10
V(g)$label <- V(g)$full_name
V(g)$label.cex <- 0.7

# Set edge attributes
E(g)$color <- ifelse(E(g)$relation == "parent", "gray", "red")
E(g)$width <- ifelse(E(g)$relation == "parent", 1, 2)
E(g)$arrow.size <- 0.5

# Create a layout that works well for family networks
layout_family <- layout_with_fr(g)

# Plot the graph
plot(g, 
     layout = layout_family,
     vertex.label.dist = 0.5,
     vertex.label.color = "black",
     vertex.label.family = "sans",
     vertex.label.degree = -pi/2,
     edge.curved = 0.2,
     main = "[Ficticious] Latin Family Network from Inscriptions")

# Add a legend
legend("bottomleft", 
       legend = c("Valerii (Family 1)", "Cornelii (Family 2)", "Tullii (Family 3)", "Male", "Female", "Parent-Child", "Spouse"),
       pch = c(15, 15, 15, 0, 1, NA, NA),
       lty = c(NA, NA, NA, NA, NA, 1, 1),
       lwd = c(NA, NA, NA, NA, NA, 1, 2),
       col = c("skyblue", "lightgreen", "salmon", "black", "black", "gray", "red"),
       pt.cex = 2,
       cex = 0.8,
       bty = "n")

## Focus on one family

Cornelii Family Focus: A more detailed view of just the Cornelii family and their marriage connections to other families.

# For example, let's focus on the Cornelii (family_id 2)

# Filter for just family 2
cornelii_ids <- family %>% 
  filter(family_id == 2) %>% 
  pull(person_id)

# Get all relationships involving Cornelii
cornelii_relations <- relationships %>%
  filter(from %in% cornelii_ids | to %in% cornelii_ids)

# Create the subgraph
g_cornelii <- graph_from_data_frame(d = cornelii_relations, 
                                   vertices = person_lookup %>% filter(person_id %in% unique(c(cornelii_relations$from, cornelii_relations$to))), 
                                   directed = TRUE)

# Set vertex attributes for the Cornelii subgraph
V(g_cornelii)$color <- ifelse(V(g_cornelii)$family_id == 2, "lightgreen", 
                             ifelse(V(g_cornelii)$family_id == 1, "skyblue", "salmon"))
V(g_cornelii)$shape <- ifelse(V(g_cornelii)$gender == "M", "square", "circle")
V(g_cornelii)$size <- 12
V(g_cornelii)$label <- V(g_cornelii)$full_name
V(g_cornelii)$label.cex <- 0.8

# Set edge attributes for the Cornelii subgraph
E(g_cornelii)$color <- ifelse(E(g_cornelii)$relation == "parent", "gray", "red")
E(g_cornelii)$width <- ifelse(E(g_cornelii)$relation == "parent", 1, 2)
E(g_cornelii)$arrow.size <- 0.5

# Create a layout that works well for family networks
layout_cornelii <- layout_with_fr(g_cornelii)

# Plot the Cornelii subgraph
plot(g_cornelii, 
     layout = layout_cornelii,
     vertex.label.dist = 0.7,
     vertex.label.color = "black",
     vertex.label.family = "sans",
     vertex.label.degree = -pi/2,
     edge.curved = 0.2,
     main = "[Ficticious] Cornelii Family Network")

# Add a legend for the Cornelii subgraph
legend("bottomleft", 
       legend = c("Cornelii", "Marriage ties to Valerii", "Marriage ties to Tullii", "Male", "Female", "Parent-Child", "Spouse"),
       pch = c(15, 15, 15, 0, 1, NA, NA),
       lty = c(NA, NA, NA, NA, NA, 1, 1),
       lwd = c(NA, NA, NA, NA, NA, 1, 2),
       col = c("lightgreen", "skyblue", "salmon", "black", "black", "gray", "red"),
       pt.cex = 2,
       cex = 0.8,
       bty = "n")

4.4 Temporal visualization showing generations

Generational View organizes the network by generations based on birth years to show how the families evolved over time.

# Add a generation attribute based on birth year
family$generation <- cut(family$birth_year, 
                            breaks = c(-120, -80, -40, 0, 40), 
                            labels = c("Generation 1", "Generation 2", "Generation 3", "Generation 4"))

# Create a generational network (simplified)
gen_network <- family %>%
  select(person_id, praenomen, nomen, cognomen, family_id, gender, generation) %>%
  mutate(full_name = paste(praenomen, nomen, cognomen))

# Create the generational graph
g_gen <- graph_from_data_frame(d = relationships, vertices = gen_network, directed = TRUE)

# Set vertex attributes for the generational graph
V(g_gen)$color <- ifelse(V(g_gen)$family_id == 1, "skyblue", 
                        ifelse(V(g_gen)$family_id == 2, "lightgreen", "salmon"))
V(g_gen)$shape <- ifelse(V(g_gen)$gender == "M", "square", "circle")
V(g_gen)$size <- 8
V(g_gen)$label <- V(g_gen)$full_name
V(g_gen)$label.cex <- 0.6

# Set edge attributes for the generational graph
E(g_gen)$color <- ifelse(E(g_gen)$relation == "parent", "gray", "red")
E(g_gen)$width <- ifelse(E(g_gen)$relation == "parent", 1, 2)
E(g_gen)$arrow.size <- 0.5

# Create a layout that separates generations
layout_gen <- layout_with_fr(g_gen)

# Plot the generational graph
plot(g_gen, 
     layout = layout_gen,
     vertex.label.dist = 0.5,
     vertex.label.color = "black",
     vertex.label.family = "sans",
     vertex.label.degree = -pi/2,
     edge.curved = 0.2,
     main = "[Ficticious] Latin Family Network Across Generations")

# Add a legend for the generational graph
legend("bottomleft", 
       legend = c("Valerii", "Cornelii", "Tullii", "Male", "Female", "Parent-Child", "Spouse"),
       pch = c(15, 15, 15, 0, 1, NA, NA),
       lty = c(NA, NA, NA, NA, NA, 1, 1),
       lwd = c(NA, NA, NA, NA, NA, 1, 2),
       col = c("skyblue", "lightgreen", "salmon", "black", "black", "gray", "red"),
       pt.cex = 2,
       cex = 0.8,
       bty = "n")

To DO: Add node size variation based on the number of connections Create an interactive version using packages like visNetwork or networkD3 Add timeline elements to show when individuals lived Incorporate the inscription data to show the archaeological evidence

5 Network analysis

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1. Aarhus University, Denmark, https://orcid.org/0000-0002-6349-0540↩︎